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Transformation Story-8

Cerebra is powering next generation industry 4.0 model for one of the largest adhesive manufacturer in the world across 133 plants.

The problem

The customer is one of the Largest adhesive manufacturer in world with 133 plants globally Adhesives used in aviation, automotive and electronic industries. The problem they faced was to increase the quality and yield of the factory lines by controlling 2 levers - viscosity and softening points.

How Cerebra Solved it

Cerebra IOT signal intelligence platform was ingested 3 years of sensor data regarding plant operations from temperature sensors, rpm sensors, torque sensors and pressure sensors which were strapped on to industrial mixers Cerebras ensemble models used to filter signal from noise and specifically identify the contributors to quality. This process was Scaled to 33 plants, 1400 manufacturing lines and 16 event types cumulatively streamed in 20 million sensor events analysed.

Impact on outcome

More than 140 million dollars of savings from defective products across 3 years.

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Transformation Story-3

Reducing cryogenic risks in LNG shipments by mining of patterns across 260 million sensor events

The Problem

Emergency Assistance & Response system was manual, expensive and time-consuming.

False alarms add to the cost and effort.

Delay in action causes avoidable loss of million dollars.

How Cerebra Solve it?

Cerebra digital solutions focus on monitoring and diagnostic tools to improve reliability and increase the quality of the customer service.

The data from LNG Platform collects on Cerebra Edge Server. Around 145 Parameter captured at a sampling frequency of 5 seconds.

The Cerebra Edge platform configured and commissioned with Heuristic's Rule and Physics Diagnostic Engine to identify any potential threats Scenarios.

On any threat scenario going ON, The Emergency Team intimated through Notification either over SMS, Email, and Real Time Application.

The duty Office leverages Cerebra Platform to analyze the scenario and relevant Sensor Signal to recommend corrective action to the Onshore Team.

Cerebra Central Application deployed on the Central Cloud Server where the historical data stored for further analysis.

The impact on outcome

Leveraged a quick response system to address the operational failure/ threat scenarios.

The Cerebra Valve Diagnostics Module evaluated the Valve functioning using a variety of diagnostics tests for Flow Characteristics, Hysteresis, Dead Time, Stiction etc.

Cerebra solution provided an indication of the malfunctioning of the valve and accurate visibility to the extent of variation which helps the operators to prioritize and minimize the downtime through exhaustive prognostic models.

The impact on outcome

Timely detection of the valve malfunction and controller re-calibration brought down operational costs.

Reduce the unplanned process downtime by earlier detection of faulty assets and incorrect configurations and can result in up to $7 million savings for every reactor in a year through reduced reactor down time

Radically improve catalyst life which is the most expensive ingredient of the entire process

Enhancing the quality of output by maintaining the operating conditions (pressure, temperature are as per controller output) in acceptable range.

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Transformation Story-6

One of the World's largest manufacturers of Shale field equipment and natural gas/diesel engines based in Houston
is heavily dependent on OEMs to minimize asset failures and downtime: There were 2 specific problems they wanted to solve.

Create new business models for Prognostics and diagnostics as a serviceHow to proactively auto generate spare part requests triggered by sensor events and thereby reduce inefficiencies

Transformation Story-9

Transformation Story-7

Cerebra triangulates signals from millions of machine events, thereby increasing the longevity of deep water assets by 40%.

The problem

Safety is one of the most complex subjects in the Oil & Gas industry.
Traditional approaches to safety such as Process Safety and Occupational
safety tend break down the subject into multiple silos and track lagging
indicators of safety rather and leading indicators. As such, last mile
real-time visibility into safety risk exposure is extremely limited, and one
of the largest hydrocarbon producers in the world was facing this very
problem.

How Cerebra Solved it

Safety Incidents happen as a result of man-machine interactions and the
processes that govern these interactions. Cerebra ingested millions of
machine signals from offshore assets, along with process, and people related
signals and quantified safety risk exposure related to Assets, Processes,
and People through sophisticated risk modeling algorithms. Signals ingested
include machine events such as leakages, pressure changes and flow count,
process related data such as audits and maintenance, and people related data
such as operator expertise.

Impact on outcome

The offshore operations team has visibility into safety risk exposure across
their rigs, and were able to ensure that they had the right information at
hand that would be necessary to take proactive action to avert potential
safety incidents due to machine failures.

One of the key problems hydroelectric plants face is that of limited PLF. Moreover, they have generation commitments with Regulatory bodies that they have to uphold to help manage peak loads, as well as fulfil general overall power demand.

Transformation Story-10

Cerebra IOT signal intelligence platform discovered voyage/asset signals for a large shipping company

Transformation Story-11

Solar OEM is using Cerebra to power energy as a service business model by mining signals from 1.2 billion panel events

Transformation Story-10

Cerebra IOT signal intelligence platform discovered voyage/asset signals for a large shipping company

The Business Problem

The largest North American Vessel Classification Society, that has classified more than 13,000 vessels is embarking on a new journey to help the vessels capture instantaneous parameters. The company then wanted to understand the parameters affecting fuel consumption, and hence predict the fuel consumption as a function of these parameters thus leading to an improved performance of the vessel.

How Cerebra Solved it

Cerebra IOT Diagnostics/Prognostics platform was connected to monitor the 70+ instantaneous parameters streaming in from various vessels every 10 seconds. Cerebra picked up those predictors that had a significant influence on the fuel consumption, so as to build a predictive model to predict the fuel consumption under given set of conditions. The model learns and improves by itself based on the new data that gets streamed. The model was also able to pick up data anomalies that led to the conclusion that certain sensors on the vessels weren’t performing as per their specifications.

With the large amount of vessel performance data collected from various vessels across different vessel categories, the Cerebra platform was further able to provide benchmarks w.r.t. Fuel consumption for similar class of vessels. These benchmarks could further be utilized while designing & building new vessels, or to identify if any retrofits could be added to improve the performance of existing vessels based on benchmarking results.